Recently, we have experienced a massive increase in the volume of
scientific articles and research artefacts (e.g., datasets, models, software packages). This
trend is expected to continue and opens up challenges including the development of large-scale
machine-readable representations of scientific knowledge, making scholarly data and knowledge
discoverable and accessible, and designing reliable and comprehensive metrics to assess
scientific impact.
Sci-K provides a forum for researchers and practitioners from different disciplines to
present, educate, and guide research related to scientific knowledge. Three themes
cover the most important challenges in this field: representation,
discoverability, and assessment.
Representation
There is a need for flexible, context-sensitive,
fine-grained, and machine-actionable representations of scholarly knowledge that are, at
the same time, structured, interlinked, and semantically rich: Scientific Knowledge
Graphs (SKGs), also known as Research Knowledge Graphs (RKGs). ...SKGs/RKGs can
power data-driven services for navigating, analysing, and making sense of research
dynamics. Current challenges are related to the design of ontologies or alternative
representation methods able to conceptualise scholarly knowledge, model its
representation, and enable exchange.
Scholarly information should be easily findable,
discoverable, and visible so that it can be mined and organised within SKGs/RKGs.
Discovery tools should be able to crawl the Web and identify scholarly data, whether on
a publisher’s website or elsewhere – institutional repositories
...
, preprint servers or open-access repositories. This is challenging and requires a
deep understanding of both the scholarly communication landscape and the needs of a
variety of stakeholders: researchers (of different fields and sub-fields),
publishers, funders, and the general public. Other challenges are related to the
discovery and extraction of entities and concepts, integration of information from
heterogeneous sources, identification of duplicates, finding connections between
entities, and identifying conceptual inconsistencies. We are particularly interested
in modern systems that integrate AI, NLP, and LLM technologies.
Due to the continuous growth in the volume of research
output, rigorous approaches for the evaluation and assessment of research impact are now
more relevant than ever. There is a need for reliable, comprehensive, and equitable
metrics and indicators of the scientific impact and merit of publications
... , datasets, research
institutions, individual researchers, and other relevant entities.
Sci-K is calling for high-quality submissions around the three main themes of research related to
scientific knowledge: representation, discoverability, and assessment.
Topics of interest include, but are not limited to:
Representation
Data models for the description of scholarly data and their
relationships.
Description and use of provenance information of scientific data.
Integration and interoperability models of different data sources.
NLP and AI approaches that demonstrate related methods and technologies.
Relevant knowledge graphs and ontologies.
Hybrid or LLM-based approaches for representation and knowledge graph
engineering.
Discoverability
Methods for extracting metadata, entities and relationships from
scientific data.
Methods for the (semi-)automatic annotation and enhancement of
scientific data.
Methods and interfaces for the exploration, retrieval, and visualisation
of scholarly data.
NLP and AI approaches that demonstrate related methods and technologies.
LLM-based systems for relevant tasks: e.g., hypothesis generation,
literature review generation and others.
Assessment
Novel methods, indicators, and metrics for quality and impact assessment
of scientific publications, datasets, software, and other relevant
entities based on scholarly data.
Uses of scientific knowledge graphs and citation networks for the
facilitation of research assessment.
Studies regarding the characteristics or the evolution of scientific
impact or merit.
NLP and AI approaches that demonstrate related methods and technologies.
Important Dates
1
Paper submission
July 11th, 2025 (23:59, AoE timezone)
Notification of acceptance
August 8th,
2025
2
3
Camera ready due
August 28th,
2025 (tentative)
Workshop day
November 2025
4
Submission guidelines
Submissions are welcome in the following categories:
Full research papers (up to 12 pages + unlimited pages of appendices and
references)
Short research papers (up to 6 pages + unlimited pages of appendices and
references)
Vision/Position papers (up to 6 pages + unlimited pages of appendices and
references)
The workshop calls for full research papers, describing original work on the listed
topics, and short papers, on early research results, new results on previously
published works, demos, and projects. In accordance with Open Science principles,
research papers may also be in the form of data or software papers (short or long
papers). Data papers present the motivation and methodology behind the creation of
data sets that are of value to the community, e.g., annotated corpora, benchmark
collections, and training sets. Software papers present software functionality, its
value for the community, and its application. To enable reproducibility and
peer-review, authors are requested to share the DOIs of datasets and software
products described in the articles.
The workshop also calls for vision/position papers providing insights towards new or
emerging areas, innovative or risky approaches, or emerging applications that will
require extensions to the state of the art. Vision papers do not necessarily have to
present results but should carefully elaborate on the motivation and ongoing
challenges of the described area.
Submissions for review must be in PDF format. They must be self-contained and written in English.
Submissions that do not follow these guidelines, or do not view or print properly, will be rejected
without review.
Sci-K will adopt a single-anonymous review process, and each paper will be reviewed by at least three
Program Committee members.
The proceedings of the workshops will be published on CEUR either as a standalone volume or in companion
proceedings of ISWC 2025.
Attending
ISWC2025 will be an in-person conference. All Sci-K papers that will be presented at the workshop and at
least one author per accepted paper must register to the conference.
Should be there any change, we will make any possible effort to inform you on time.
Program Committee
Tentative list and in alphabetical order.
Miriam Albers (ZB MED)
Miriam Baglioni (ISTI - CNR)
Francisco Bolanos (UEES)
Leyla Jael Castro (ZB MED Information Centre for Life Sciences)
Serafeim Chatzopoulos (Athena Research Center)
Patricia Feeney (CrossRef)
Daniel Garijo (Universidad Politécnica de Madrid)
Mohamad Yaser Jaradeh (L3S Research Center, Leibniz University Hannover)
Alireza Javadian Sabet (University of Pittsburgh)
Mostafa Karami (University of Connecticut)
Paris Koloveas (Athena Research Center)
Anastasia Krithara (NCSR "Demokritos")
Shubhanshu Mishra (University of Illinois at Urbana-Champaign)
Allard Oelen (L3S Research Center, Leibniz University Hannover)
David Pride (KMi, The Open University)
Jodi Schneider (University of Illinois Urbana Champaign)
Christos Tryfonopoulos (University of Peloponnese)
Organising Committee
Co-chairs for Sci-K 2025 (alphabetically)
Anna Jacyszyn
FIZ Karlsruhe – Leibniz-Institute for Information Infrastructure,
Eggenstein-Leopoldshafen (DE)
Andrea Mannocci
Italian Research Council (CNR), Pisa (IT)
Francesco Osborne
The Open University, Milton Keynes (UK)
Georg Rehm
DFKI,
Berlin (DE)
Angelo A. Salatino
The Open University, Milton Keynes (UK)
Sonja Schimmler
TU Berlin, Fraunhofer FOKUS, Berlin (DE)
Lise Stork
University of Amsterdam, Amsterdam (NL)
Steering Committee
Alphabetically ordered
Paolo Manghi
Italian Research Council (CNR), Pisa (IT)
Dimitris Sacharidis
Université Libre de Bruxelles (ULB), Brussels (BE)
Held in conjunction with the International Semantic Web Conference (ISWC) 2025,
Nara (Japan)
Aim and Scope
Recently, we have experienced a massive increase in the volume of scientific articles
and research artefacts (e.g., datasets, models, software packages). This trend is
expected to continue and opens up challenges including the development of
large-scale machine-readable representations of scientific knowledge, making
scholarly data and knowledge discoverable and accessible, and designing reliable and
comprehensive metrics to assess scientific impact. Sci-K provides a forum for
researchers and practitioners from different disciplines to present, educate,
and guide research related to scientific knowledge. Three themes cover the
most important challenges in this field:
Representation. There is a need for flexible, context-sensitive,
fine-grained, and machine-actionable representations of scholarly knowledge that
are, at the same time, structured, interlinked, and semantically rich: Scientific
Knowledge Graphs (SKGs), also known as Research Knowledge Graphs (RKGs). SKGs/RKGs
can power data-driven services for navigating, analysing, and making sense of
research dynamics. Current challenges are related to the design of ontologies or
alternative representation methods able to conceptualise scholarly knowledge, model
its representation, and enable exchange.
Discoverability. Scholarly information should be easily findable,
discoverable, and visible so that it can be mined and organised within SKGs/RKGs.
Discovery tools should be able to crawl the Web and identify scholarly data, whether
on a publisher’s website or elsewhere – institutional repositories, preprint servers
or open-access repositories. This is challenging and requires a deep understanding
of both the scholarly communication landscape and the needs of a variety of
stakeholders: researchers (of different fields and sub-fields), publishers, funders,
and the general public. Other challenges are related to the discovery and extraction
of entities and concepts, integration of information from heterogeneous sources,
identification of duplicates, finding connections between entities, and identifying
conceptual inconsistencies. We are particularly interested in modern systems that
integrate AI, NLP, and LLM technologies.
Assessment. Due to the continuous growth in the volume of research output,
rigorous approaches for the evaluation and assessment of research impact are now
more relevant than ever. There is a need for reliable, comprehensive, and equitable
metrics and indicators of the scientific impact and merit of publications, datasets,
research institutions, individual researchers, and other relevant entities.
Topics of Interest
We encourage the submission of papers covering, but not limited to, one or more of
the following topics:
Representation
Data models for the description of scholarly data and their
relationships.
Description and use of provenance information of scientific data.
Integration and interoperability models of different data sources.
NLP and AI approaches that demonstrate related methods and technologies.
Relevant knowledge graphs and ontologies.
Hybrid or LLM-based approaches for representation and knowledge graph
engineering.
Discoverability
Methods for extracting metadata, entities and relationships from
scientific data.
Methods for the (semi-)automatic annotation and enhancement of
scientific data.
Methods and interfaces for the exploration, retrieval, and visualisation
of scholarly data.
NLP and AI approaches that demonstrate related methods and technologies.
LLM-based systems for relevant tasks: e.g., hypothesis generation,
literature review generation and others.
Assessment
Novel methods, indicators, and metrics for quality and impact assessment
of scientific publications, datasets, software, and other relevant
entities based on scholarly data.
Uses of scientific knowledge graphs and citation networks for the
facilitation of research assessment.
Studies regarding the characteristics or the evolution of scientific
impact or merit.
NLP and AI approaches that demonstrate related methods and technologies.
Submission Guidelines
Submissions are welcome in the following categories:
Full research papers (up to 12 pages + unlimited pages of appendices and
references)
Short research papers (up to 6 pages + unlimited pages of appendices and
references)
Vision/Position papers (up to 6 pages + unlimited pages of appendices and
references)
The workshop calls for full research papers, describing original work on the listed
topics, and short papers, on early research results, new results on previously
published works, demos, and projects. In accordance with Open Science principles,
research papers may also be in the form of data or software papers (short or long
papers). Data papers present the motivation and methodology behind the creation of
data sets that are of value to the community, e.g., annotated corpora, benchmark
collections, and training sets. Software papers present software functionality, its
value for the community, and its application. To enable reproducibility and
peer-review, authors are requested to share the DOIs of datasets and software
products described in the articles.
The workshop also calls for vision/position papers providing insights towards new or
emerging areas, innovative or risky approaches, or emerging applications that will
require extensions to the state of the art. Vision papers do not necessarily have to
present results but should carefully elaborate on the motivation and ongoing
challenges of the described area.
Sci-K will adopt a single-anonymous review process and each paper will be reviewed
by at least three Program Committee members.
Submissions must be in PDF format and must adhere to the CEURART single-column
template. Submissions that do not follow these guidelines, or do not view or print
properly, may be rejected without review. You can download an offline version with
the
style
files from http://ceur-ws.org/Vol-XXX/CEURART.zip.
It also contains DOCX template files. Overleaf users may want to use the
CEURART template available in Overleaf. Please adhere also to the CEUR-WS Policy on
AI-Assisting Tools.
The proceedings of the workshops will be published on CEUR (indexed in Scopus, DBLP
and so on.)
Paper submission:July 11th, 2025
(23:59, AoE timezone) Notification of acceptance:August 8th,
2025 Camera-ready due:August 28th, 2025 Workshop day:November 2025 (to be
confirmed)
Organisation
Anna Jacyszyn, FIZ Karlsruhe, DE Andrea Mannocci, CNR-ISTI, IT Francesco Osborne, The Open University, UK Georg Rehm, DFKI, DE Angelo Salatino, The Open University, UK Sonja Schimmler, TU Berlin, Fraunhofer FOKUS, DE Lise Stork, University of Amsterdam, NL
